www.frontiersin.org March 2011 | Volume 5 | Article 161 | 1

Original research articlepublished: 31 March 2011

doi: 10.3389/fnins.2011.00161

Transcriptomic analysis in a Drosophila model identifies previously implicated and novel pathways in the therapeutic mechanism in neuropsychiatric disorders

Priyanka Singh†, Farhan Mohammad† and Abhay Sharma*

Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, Delhi University Campus, Delhi, India

We have taken advantage of a newly described Drosophila model to gain insights into the potential mechanism of antiepileptic drugs (AEDs), a group of drugs that are widely used in the treatment of several neurological and psychiatric conditions besides epilepsy. In the recently described Drosophila model that is inspired by pentylenetetrazole (PTZ) induced kindling epileptogenesis in rodents, chronic PTZ treatment for 7 days causes a decreased climbing speed and an altered CNS transcriptome, with the latter mimicking gene expression alterations reported in epileptogenesis. In the model, an increased climbing speed is further observed 7 days after withdrawal from chronic PTZ. We used this post-PTZ withdrawal regime to identify potential AED mechanism. In this regime, treatment with each of the five AEDs tested, namely, ethosuximide, gabapentin, vigabatrin, sodium valproate, and levetiracetam, resulted in rescuing of the altered climbing behavior. The AEDs also normalized PTZ withdrawal induced transcriptomic perturbation in fly heads; whereas AED untreated flies showed a large number of up- and down-regulated genes which were enriched in several processes including gene expression and cell communication, the AED treated flies showed differential expression of only a small number of genes that did not enrich gene expression and cell communication processes. Gene expression and cell communication related upregulated genes in AED untreated flies overrepresented several pathways – spliceosome, RNA degradation, and ribosome in the former category, and inositol phosphate metabolism, phosphatidylinositol signaling, endocytosis, and hedgehog signaling in the latter. Transcriptome remodeling effect of AEDs was overall confirmed by microarray clustering that clearly separated the profiles of AED treated and untreated flies. Besides being consistent with previously implicated pathways, our results provide evidence for a role of other pathways in psychiatric drug mechanism. Overall, we provide an amenable model to understand neuropsychiatric mechanism in cellular and molecular terms.

Keywords: pentylenetetrazole, valproate, gabapentin, levetiracetam, vigabatrin, ethosuximide, antiepileptic, transcriptome

and MAP kinase pathways, and that genes in these pathways show increased expression in the brains of rodents exposed to antide-pressant treatments (Altar et al., 2009).

As with other psychiatric drugs, the long term mechanism of antiepileptic drugs (AEDs), which are also used in the treatment of various psychiatric conditions besides epilepsy, is poorly understood (Rogawski and Loscher, 2004; Johannessen, 2008; Kuzniecky et al., 2008; Nagarkatti et al., 2008; Nalivaeva et al., 2009). Conceptually, transcriptomic analysis in established mammalian models of epilepsy and AED testing can be used to gain insights into the mechanisms of action of these drugs. However, inherent complexity of mamma-lian brain does not render these established models as amenable to systems modeling (Gorter et al., 2006). Under these circumstances, the genetically tractable model organism Drosophila, because of its amenability to a wide variety of experimental approaches, including functional genomics (Chintapalli et al., 2007), may offer an attrac-tive system to unravel AED mechanism.

An established model of epileptogenesis and AED testing, kin-dling in rodents involves long term brain plasticity in which recur-rent activation of neural pathways through chemical or electrical means results in an increased susceptibility to evoked seizures that

IntroductIonDrugs used in the treatment of psychiatric disorders are mostly known to target neurotransmitter receptors. This receptor mecha-nism alone however does not provide simple mechanistic inter-pretations for their long term clinical efficacy (Molteni et al., 2009; Zhou et al., 2009). In addition to receptor mediated acute biochemical effects that may explain short-term clinical response, these drugs are considered to exert other long term therapeutic effects that may not be directly related to receptor mechanisms (Molteni et al., 2009). These long term neuroprotective mecha-nisms underlying psychiatric drug action are however poorly understood (McLoughlin et al., 2009). It has been suggested that drug induced changes in gene and protein expression may ultimately translate into the overall neuroprotection. Given this, genome level expression analysis is considered to offer a prom-ising approach to identify genes and pathways underlying neu-ropsychiatric conditions and mechanisms of drug action (Altar et al., 2009). For example, a meta-analysis has recently revealed that gene expression profiles of brains from persons with major depressive disorder show decreased expression of genes related to glutamate transport and metabolism, neurotrophic signaling

Edited by:Jocelyne Caboche, Université Pierre et Marie Curie, France

Reviewed by:Robert W. Williams, University of Tennessee Health Science Center, USASerge Birman, CNRS–ESPCI, France

*Correspondence:Abhay Sharma, Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, Mall Road, Delhi University Campus, Delhi 110007, India. e-mail: abhaysharma@igib.res.in†Present address:Priyanka Singh, Gènes et Dynamique des Systèmes de Mémoire, CNRS UMR 7637, ESPCI, 10 rue Vauquelin, 75005 Paris, France;Farhan Mohammad, The Wellcome Trust Centre for Human Genetics Roosevelt Drive, Oxford OX3 7BN, UK.

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Drosophila model offered a simpler system for identifying poten-tial mechanism of AEDs at transcriptomic level because it is not complicated by continued presence of the GABA

A antagonist. We

thus used the post-PTZ regime in the present analysis. Specifically, we tested the concept that if treatment with AEDs after PTZ with-drawal is found to rescue flies from developing increased climbing speed then analyzing CNS expression profiles of flies treated and untreated with AEDs may enable identification of transcriptomic correlates of AED action.

MaterIals and MethodsBehavIoral pharMacologyThe wild type Oregon-R strain of Drosophila melanogaster was used in the analysis. Cultures were routinely maintained at 24 ± 1°C, 60% RH, and 12 h light (9 AM to 9 PM) and 12 h dark cycle, in normal food (NF) consisting of agar–agar, maize powder, brown sugar, dried yeast, and nipagin. Standard fly handling and manipulation methods were followed. Stringency required in behavioral studies was strictly adhered to at several levels including housing condi-tions, exposure to anesthetic agent, light intensity, etc. Three- to 4-day-old unmated male flies were used to begin treatment at 0 day time-point (Figure 1A). Final concentration of PTZ, ethosuximide (ETH), gabapentin (GBP), vigabatrin (VGB), sodium valproate (NaVP; all from Sigma-Aldrich), and levetiracetam (LEV; Levesam 500, Nicholas Piramal) in the fly medium was 8, 3.48, 16, 24, 0.33, and 5 mg/ml, in that order. Climbing speed was measured using a semi-manual method (Mohammad et al., 2009). In this method, individual flies were first familiarized in a vertically placed glass column for 90 s and then startle-induced climbing activity was recorded using a “dot/comma” method. In “dot/comma” record-ing, the locomotor activity of a fly was recorded by keep pressing the dot key or the comma key of a personal computer, to record a climbing or a resting fly, in that order. Using the cursor speed,

ultimately progresses to spontaneous seizures (Goddard et al., 1969; Walker et al., 2002; Husum et al., 2004; Garriga-Canut et al., 2006; McNamara et al., 2006; De Smedt et al., 2007). Like epileptogen-esis, kindling is known to be associated with several non-epileptic conditions such as schizophrenia-like behaviors, hyperlocomo-tor activity, anxiogenic response, hyperalgesia, amnesia, spatial learning and memory, and neurodegeneration (Mortazavi et al., 2005; Pavlova et al., 2006; Szyndler et al., 2006; Akula et al., 2007; Howland et al., 2007; Omrani et al., 2007; Ma and Leung, 2010). Notably, inspired by pentylenetetrazole (PTZ) kindling in rodents, we recently described a Drosophila model of chronic PTZ induced alteration in locomotor activity (Mohammad et al., 2009), a behav-ior that is considered relevant in understanding neuropsychiatric conditions (Yamamoto et al., 2008; Iliadi, 2009). In this fly model, 7 days of PTZ treatment and 7 days of subsequent PTZ discontinu-ation progressively result in a decreased and an increased speed of startle-induced climbing in Drosophila adult, in that order. The chronic PTZ regime is responsive to AEDs; flies treated with PTZ and AED combined do not exhibit altered locomotor behavior. Importantly, downregulation of genes enriched in several proc-esses such as transcription, cell differentiation, cell communica-tion, neurogenesis, axonogenesis, axon guidance, and glutamate metabolism, etc., characterize the fly head transcriptome in the chronic PTZ regime. Moreover, gene expression alteration in the fly model has been found to mimic that reported in established mam-malian models of epileptogenesis and human epileptic patients (Mohammad et al., 2009). These findings have suggested that the fly model may potentially be used in understanding mechanisms of action of AEDs at transcriptomic level.

Here, we describe use of the fly model to gain insights into the possible mechanism of AED action. Unlike chronic PTZ regime characterized earlier at transcriptomic level (Mohammad et al., 2009), the uncharacterized post-PTZ withdrawal part of the

Figure 1 | Study design for behavioral and transcriptomic analyses. Twelve different treatment conditions for behavioral analysis are depicted (A). Climbing speed was measured at 14th day time-point. Six treatment combinations for gene

expression comparison are shown (B). Expression profiles were generated at 10th and 14th day time-points. NF, normal food; ETH, ethosuximide; GBP, gabapentin; VGB, vigabatrin; NaVP, sodium valproate, LEV, levetiracetam. See text for details.

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mixture was denatured at 65°C and applied onto microarray slides. The slides were covered by a 24 mm × 60 mm coverslip (ESCO, Portsmouth, USA). Hybridization was carried out in a hybridiza-tion chamber (Corning) at 37°C for 16 h. After hybridization, slides were submerged in a solution containing 1× SSC and 0.1% SDS at 50°C, to remove the coverslips. Slides were washed in 1× SSC and 0.1% SDS at 50°C (three times for 15 min each) and then in 1× SSC at room temperature (twice for 15 min each). Slides were further washed in 0.1× SSC for 15 min and the liquid remaining on the slides after washing was quickly removed by spinning the slides at 600 rpm for 5 min.

Slides were scanned at 10 μm resolution using GenePix 4000A Microarray Scanner (Molecular Devices) and the images preproc-essed and quantified using Gene Pix Pro 6.0 (Molecular Devices). Ratio based data normalization and selection of features were performed using Acuity 4.0 (Molecular Devices). All Spots with raw intensity less then 100 U and less then twice the average back-ground was ignored during normalization. Normalized data was filtered for the selection of features before further analysis. Only those spot were selected which contained a small percentage (<3) of saturated pixels, were not flagged bad or found absent (flags > 0), had relatively uniform intensity and uniform background [Rgn R2 (635/532) > 0.6] and were detectable above background (SNR > 3). Analyzable spots in at least three of four biological replicates per-formed were retrieved for downstream analysis using significant analysis of microarrays (SAM 3.0, Excel Add-In), under the con-ditions of one class response and 100 permutations (Tusher et al., 2001). Normalized log

2 ratio (635/532) of four biological replicates

with balanced dye-swaps was used for microarray clustering using Acuity 4.0 (Molecular Devices). Details of RNA extraction and microarray analysis have been described previously (Mohammad et al., 2009). The full microarray data set has been deposited in the Gene Expression Omnibus1 under accession series GSE7156, GSE10984, GSE10985, GSE10986, GSE10987, and GSE10988.

the dots and commas were accordingly transformed in the activity and rest period. Climbing speed was calculated using the following formula, s = h/t, where s = climbing speed, h = height climbed in centimeter, and t = activity period in second.

MIcroarray analysIsTotal cellular RNA was isolated from fly heads belonging to four biological replicates. Microarray – cDNA Synthesis Kit, – Target Purification Kit, and – RNA Target Synthesis Kit (Roche) were used to generate labeled antisense RNA. Starting with 10 μg of total cellular RNA, Eberwine method (kits from Roche) was used to generate cDNA and thereafter Cy3 and Cy5 (Amersham) labeled antisense RNA. The Cy3 and Cy5 labeled aRNAs (control and treated) were pooled together and precipitated, washed, air-dried, and dissolved in 18 MΩ RNAase free water. A total of 48 microarrays were hybridized, four each for 10th and 14th day flies not treated with any AED and 10th and 14th day flies treated with each of the five AEDs separately (Figure 1B). The arrays used in the experiment (Canadian Drosophila Microarray Centre, Toronto) represent over 10000 unique D. melanogaster genes and are available for distribution to academic labs. Each microarray compared RNA abundance in drug exposed flies versus flies never exposed to any drug at any time, i.e., maintained throughout in NF. Out of four slides representing four biological replicates, two were dye-swaps. Each biological replicate represented RNA isolated from heads of 120 control or treated flies. These flies were collected from four vials, with 30 flies housed in each. The control and drug fed flies were always treated in parallel. The four biological replicates represented control and treated flies collected on four different days. Microarray hybridization was set-up on eight different days, four each for 10th and 14th day time-points. A single biological replicate of each of the six comparisons to be carried out for a given time-point were proc-essed together and used for hybridization in parallel, on a single day (Figure 2). Hybridization solution contained hybridization buffer (DIG Easy Hyb, Roche), 10 mg/ml salmon testis DNA (0.05 mg/ml final concentration, Sigma), 10 mg/ml yeast tRNA (0.05 mg/ml final concentration, Sigma), and the Cy3 and Cy5 labeled product. The

Figure 2 | Batch structure of microarrays. Microarrays were run in eight batches. Each batch comprised of one of the four biological replicates belonging to 12 comparisons depicted in Figure 1. Fly treatment, RNA isolation, labeling, hybridization, and scanning were carried out separately for each of the eight batches.

1http://www.ncbi.nlm.nih.gov/geo/

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reliable. In the present analysis, we used Student’s t-test with nominal p-value for pair-wise comparisons. As expected from the previous report (Mohammad et al., 2009), PTZ withdrawal alone caused an increased climbing speed compared to NF (Figure 3). However, climbing speed of flies treated with either of the AEDs except ETH did not differ significantly from that of NF control. In ETH group also, though the flies showed higher speed than NF (p = 0.013), the difference was far less significant compared to that observed between PTZ withdrawal alone and NF (p = 0.00000017). In AED alone group, none except VGB caused a significantly altered speed. The observed rescuing effect of GBP, NaVP, and LEV in post-PTZ with-drawal flies was thus found not to be confounded by their locomotor effect in normally grown flies. Cumulatively, the AEDs in general normalized the behavioral perturbation caused by PTZ withdrawal.

transcrIptoMIc effect of ptZ Is norMalIZed By aedsDifferentially expressed genesWe next asked the question if AEDs in general normalize the tran-scriptomic perturbation caused by PTZ withdrawal. To explore this, microarray gene expression profiles of fly heads were generated at two time-points – 3rd and 7th day after PTZ withdrawal, i.e., on 10th and 14th day from the start of PTZ treatment. Twelve sets of flies were profiled, six each for 10th and 14th day time-points (Figure 2). In one set each for the two time-points, flies were not treated with any AED after PTZ withdrawal (Figure 2). In five other sets for each time-points, flies were treated with PTZ for 7 days, with one of the five AEDs for 3 days (10th day time-point), or with NF for 4 days (14th day time-point), in that order (Figure 2). Flies treated in par-allel with NF throughout were used for comparison, in each of the 12 sets of microarrays. Four biological replicates comprised each set of microarray; one set compared PTZ withdrawal with NF control, and the rest compared ETH, GBP, VGB, NaVP, or LEV treatment after PTZ withdrawal with NF control (Figure 2). In a preliminary analysis, we observed an increasing enrichment of GO biological processes in differentially expressed genes up to 15% false discovery rate (FDR). Previously, a control microarray experiment that used the same method which was followed here compared NF versus NF flies and reported no differentially expressed gene below 96% FDR (Mohammad et al., 2009). Considering the above, we used 15% FDR cut-off for identifying differentially expressed genes. Genes were found to be differentially expressed in all the six sets of microarrays, in both 10th and 14th day time-points. The up- and down-regulated

BIoInforMatIcsFLIGHT2 was used for retrieving gene symbols and IDs. The GOTool Box (Martin et al., 2004) was used to retrieve overrepresented gene ontology (GO) biological processes in up- or down-regulated genes. The GOTool Box was used under the settings, ontology, biological process; mode, all terms; reference, genome; evidence, all–all evidence; species, D. melanogaster; GO-stats3. DAVID4 was used for examining enrichment of pathways in Kyoto encyclopedia of genes and genomes (KEGG) database using modified Fisher exact test (Dennis et al., 2003; Huang et al., 2009). Genes showing pathway enrichment in DAVID analysis were depicted in the KEGG maps for D. melanogaster5.

resultsBehavIoral effect of ptZ Is norMalIZed By aedsWe first examined the behavioral pharmacology of AEDs in the fly model. In this analysis, flies were treated with PTZ for 7 days, with an AED for 3 days, and with NF for 4 days, in that order, before climb-ing speed was measured (Figure 1A). In parallel, flies were treated either with NF for the entire 14 days, or with PTZ for first 7 days and then by NF for rest of the period, or with NF, AED, and NF for 7, 3, and 4 days in sequence (Figure 1A). Previously, a control climbing assay using the same method followed here detected no significant variation among various batches of NF flies housed in different vials, neither in one-way ANOVA nor in two-tailed, pair-wise Student’s t-test with p-value unadjusted for multiple testing (Mohammad et al., 2009). This demonstrated that climbing speed measurement was a robust assay and pair-wise comparison at nominal p-value is

Figure 3 | Behavioral pharmacology of AeDs. Mean ± SE (n = 24) of climbing speed of flies treated and untreated with AED after PTZ withdrawal, and treated with AED alone. Note that climbing speed of flies treated with an AED after PTZ withdrawal is either insignificantly or less significantly different from the control (NF) group, compared to flies not treated with an AED after PTZ withdrawal. PTZ indicates no AED treatment after PTZ withdrawal, AED abbreviation indicates AED alone treatment, PTZ + AED abbreviation indicates AED treatment after PTZ withdrawal. Speed in the control (NF) group was compared with various treatment groups, in pair-wise comparisons. *Indicates nominal p-value. See text for details.

Table 1 | Numbers of SAM analyzable spot iDs and differentially

expressed genes in microarray profiles of flies treated with or without

AeDs after PTZ withdrawal.

No AeD eTH gBP VgB NaVP LeV

10TH DAyAnalyzable spots 7877 9450 8593 7443 8775 2760Upregulated genes 929 1 0 0 0 4Downregulated genes 49 0 8 283 1 4

14TH DAyAnalyzable spots 6353 5107 4473 5609 5505 2651Upregulated genes 48 0 0 42 0 0

Downregulated genes 158 7 203 648 104 5

2http://www.flight.licr.org/search/batch_homology.jsp3http://burgundy.cmmt.ubc.ca/GOToolBox/4http://david.abcc.ncifcrf.gov/home.jsp5http://www.genome.jp/kegg/tool/color_pathway.html

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Table 2 | enriched gO processes in differentially expressed genes in flies treated with or without AeDs after PTZ withdrawal.

gO_iD Term p-value*

10TH DAy, uPreguLATeD, PTZ wiTHDrAwAL, NO AeD

GO:0008152 Metabolic process 3.53E-09GO:0044238 Primary metabolic process 3.96E-09GO:0006139 Nucleobase, nucleoside, nucleotide, 2.48E-07 and nucleic acid metabolic process GO:0043283 Biopolymer metabolic process 6.22E-07GO:0044237 Cellular metabolic process 1.28E-06GO:0043170 Macromolecule metabolic process 1.58E-06GO:0009987 Cellular process 1.06E-05GO:0034960 Cellular biopolymer metabolic process 2.23E-05GO:0009058 Biosynthetic process 5.58E-05GO:0044249 Cellular biosynthetic process 6.06E-05GO:0044260 Cellular macromolecule metabolic process 7.72E-05GO:0034961 Cellular biopolymer biosynthetic process 0.0001GO:0043284 Biopolymer biosynthetic process 0.0001gO:0006350 Transcription 0.00016

gO:0010467 gene expression 0.00017

GO:0065007 Biological regulation 0.0002GO:0050794 Regulation of cellular process 0.0004gO:0006366 Transcription from rNA 0.0004

polymerase ii promoter

GO:0034645 Cellular macromolecule 0.0006 biosynthetic processGO:0009059 Macromolecule biosynthetic process 0.0006gO:0016070 rNA metabolic process 0.0008

GO:0050789 Regulation of biological process 0.001GO:0010556 Regulation of macromolecule 0.003 biosynthetic process gO:0006357 regulation of transcription from 0.004

rNA polymerase ii promoter

GO:0065003 Macromolecular complex assembly 0.004GO:0031326 Regulation of cellular biosynthetic process 0.004GO:0009889 Regulation of biosynthetic process 0.004GO:0043933 Macromolecular complex 0.005 subunit organization gO:0045449 regulation of transcription 0.007

gO:0006351 Transcription, DNA-dependent 0.007

gO:0032774 rNA biosynthetic process 0.008

GO:0019219 Regulation of nucleobase, nucleoside, 0.009 nucleotide, and nucleic acid metabolic processgO:0010468 regulation of gene expression 0.012

GO:0034621 Cellular macromolecular 0.017 complex subunit organizationGO:0080090 Regulation of primary metabolic process 0.021gO:0007154 Cell communication 0.031

GO:0034622 Cellular macromolecular complex assembly 0.036GO:0060255 Regulation of macromolecule 0.036 metabolic process

10TH DAy, DOwNreguLATeD, PTZ wiTHDrAwAL, NO AeDGO:0044237 Cellular metabolic process 0.024

10TH DAy, DOwNreguLATeD, PTZ wiTHDrAwAL, gBPGO:0006629 Lipid metabolic process 0.016GO:0009636 Response to toxin 0.05

gO_iD Term p-value*

10TH DAy, DOwNreguLATeD, PTZ wiTHDrAwAL, LeVGO:0048252 Lauric acid metabolic process 0.002GO:0031000 Response to caffeine 0.007GO:0014074 Response to purine 0.007GO:0009404 Toxin metabolic process 0.02GO:0017143 Insecticide metabolic process 0.02GO:0006805 Xenobiotic metabolic process 0.02GO:0009410 Response to xenobiotic stimulus 0.02GO:0014070 Response to organic cyclic substance 0.04GO:0043279 Response to alkaloid 0.04GO:0017085 Response to insecticide 0.046

14TH DAy, DOwNreguLATeD, PTZ wiTHDrAwAL, eTHGO:0009636 Response to toxin 0.0051

14TH DAy, DOwNreguLATeD, PTZ wiTHDrAwAL, gBPGO:0008152 Metabolic process 3.36E-06GO:0016052 Carbohydrate catabolic process 0.00012GO:0046164 Alcohol catabolic process 0.0024GO:0050896 Response to stimulus 0.0053GO:0006091 Generation of precursor 0.006 metabolites and energy GO:0044237 Cellular metabolic process 0.006GO:0009636 Response to toxin 0.02GO:0044275 Cellular carbohydrate catabolic process 0.03GO:0044248 Cellular catabolic process 0.03

14TH DAy, uPreguLATeD, PTZ wiTHDrAwAL, VgBGO:0014866 Skeletal myofibril assembly 0.047

14TH DAy, DOwNreguLATeD, PTZ wiTHDrAwAL, VgBGO:0008152 Metabolic process 6.04E-11GO:0000022 Mitotic spindle elongation 5.01E-07GO:0051231 Spindle elongation 6.31E-07GO:0044237 Cellular metabolic process 1.37E-06GO:0044238 Primary metabolic process 3.30E-05GO:0019538 Protein metabolic process 6.66E-05GO:0044267 Cellular protein metabolic process 9.63E-05GO:0044248 Cellular catabolic process 0.0006GO:0006412 translation 0.0007GO:0007052 Mitotic spindle organization 0.001GO:0050896 Response to stimulus 0.002GO:0006950 Response to stress 0.005GO:0009056 Catabolic process 0.009GO:0007051 Spindle organization 0.03

14TH DAy, DOwNreguLATeD, PTZ wiTHDrAwAL, NAVPGO:0009636 Response to toxin 2.86E-06GO:0050896 Response to stimulus 0.0003GO:0009056 Catabolic process 0.0003GO:0016052 Carbohydrate catabolic process 0.004GO:0005975 Carbohydrate metabolic process 0.008GO:0008152 Metabolic process 0.03

14TH DAy, DOwNreguLATeD, PTZ wiTHDrAwAL, LeVGO:0015782 CMP-sialic acid transport 0.03GO:0015789 UDP-N-acetylgalactosamine transport 0.03

GO:0015757 Galactose transport 0.03

Gene expression and cell communication related processes are highlighted in bold.*After Bonferroni correction.

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genes are listed in Table S1 in Supplementary Material. The numbers of differentially expressed genes and the total analyzable spots in significant analysis of microarrays (SAM) are provided in Table 1. Differentially expressed genes in AED untreated flies (Table S1 in Supplementary Material) showed significant overlap between 10th and 14th day, in a direction-specific manner: 20 genes were common between 929 upregulated genes on 10th day and 48 upregulated genes on 14th day; 10 genes were common between 49 downregulated genes on 10th day and 158 downregulated genes on 14th day. Considering the total number of unique genes represented in the arrays as 10500, these overlaps were extremely significant (hypergeometric distribu-tion p = 9E-10 and 2E-09, in that order). These results were not surprising because a significant overlap is expected at adjacent time-points. Very small number of differentially expressed genes (or no such genes) precluded similar analysis for AED treated profiles. Overall, the above analysis proved the robustness of the array data.

Table 3 | enriched processes in gene expression and cell communication

related upregulated genes in AeD untreated flies.

Term p-value*

geNe exPreSSiON

dme03040:spliceosome 0.001

dme03018:RNA degradation 0.006

dme03010:ribosome 0.049

CeLL COMMuNiCATiON

dme00562:inositol phosphate metabolism 0.012

dme04070:phosphatidylinositol signaling system 0.02

dme04144:endocytosis 0.027

dme04340:hedgehog signaling pathway 0.046

*Nominal.

Figure 4 | genes mapping to Kegg pathway for spliceosome. Pink boxes represent upregulated genes in AED untreated flies. These genes are Hsp68 (Heat shock protein 68), U2af50 (U2 small nuclear riboprotein auxiliary factor 50),

snRNP69D (small nuclear ribonucleoprotein at 69D), and snRNP70K (small nuclear ribonucleoprotein 70K). Green boxes represent other members in the pathway database for Drosophila melanogaster.

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to flies without AED treatment, the GBP and VGB treated flies displayed higher number of differentially expressed genes on 14th day. Together, AED treatment in general was found to reduce the number of differentially expressed genes on 10th day, i.e., the time-point till which flies were treated with the AEDs.

Process enrichment analysisWe next examined enrichment of GO biological processes in the differentially expressed genes. In AED untreated flies, the genes upregulated on 10th day showed enrichment for GO processes

Using 2 × 2 chi-square test with Yates’ correction for continuity, we compared the number of analyzable spots and the number of up- and down-regulated genes in flies without AED treatment with corresponding numbers in AED treated flies. A pair-wise compari-son revealed extremely significant difference in these numbers, for 10th as well as 14th day time-points. The p-values obtained for all the 10 comparisons, five each for 10th and 14th day time-points, were in the range of 0.00–0.0009. All comparisons except that with 14th day GBP and 14th day VGB showed reduction in the number of differentially expressed genes by AEDs. Compared

Figure 5 | genes mapping to Kegg pathway for rNA degradation. Pink boxes represent upregulated genes in AED untreated flies. These genes are Csl4 (CG6249 gene product from transcript CG6249-RA), Dis3 (CG6413 gene

product from transcript CG6413-RA), and Rrp6 (CG7292 gene product from transcript CG7292-RB). Green boxes represent other members in the pathway database for Drosophila melanogaster.

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observation (Table 1) that AEDs in general normalize the transcrip-tomic perturbation, the GO enrichment analysis further suggested that AEDs cause normalization of perturbation in several processes including gene expression and cell communication.

Pathway enrichment analysisTo gain further insights into the normalizing effect of AEDs on PTZ withdrawal induced transcriptomic perturbation, we next examined if gene expression (GO:0010467) and cell communica-tion (GO:0007154) related upregulated genes in AED untreated flies on 10th day enrich specific pathways. Notably, several path-ways were found to be overrepresented in the two sets of genes (Table 3) – the gene set belonging to gene expression category showed enrichment for spliceosome, RNA degradation and ribos-ome, and the gene set belonging to cell communication enriched inositol phosphate metabolism, phosphatidylinositol signaling, endocytosis, and hedgehog signaling. The upregulated genes are mapped on to KEGG pathways for visualization (Figures 4–10).

related to metabolism, gene expression and cell communication, and the downregulated genes that for cellular metabolic process (Table 2). Neither upregulated nor downregulated gene set repre-senting 14th day time-point showed enrichment for any process. In AED treated flies, differentially expressed genes in 10th day showed enrichment of processes only in the flies treated with GBP and LEV, not ETH, VGB, and NaVP (Table 2). These enriched processes were related to metabolism. In 14th day time-point, genes differentially regulated by AEDs were found to enrich various processes. These processes were related to metabolism, response to toxin, skeletal myofibril assembly, spindle elongation, translation, CMP-sialic acid transport, etc. (Table 2). Notably, gene expression and several related processes such as transcription, transcription from RNA polymerase II promoter, RNA metabolic process, RNA biosynthetic process etc. were enriched in genes upregulated in flies that were not treated with any AED (Table 2). Also notable was the enrich-ment of cell communication, besides others, in genes upregulated in AED untreated group (Table 2). Combined with the previous

Figure 6 | genes mapping to Kegg pathway for ribosome. Pink boxes represent upregulated genes in AED untreated flies. These genes are RpL15 (Ribosomal protein L15), RpL28 (Ribosomal protein L28), RpL38 (Ribosomal

protein L38), RpL39 (Ribosomal protein L39), and RpL5 (Ribosomal protein L5). Green boxes represent other members in the pathway database for Drosophila melanogaster.

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Microarray clusteringTranscriptomic analysis so far depended exclusively on differ-entially expressed genes selected using a FDR cut-off. To further examine if AEDs indeed remodel PTZ withdrawal induced tran-scriptomic perturbation, we next clustered the microarray profiles of flies with or without AED treatment. The hierarchical cluster-ing that uses expression values of all the analyzable spots in the microarrays clearly separated the time series profiles of AED treated and untreated flies (Figure 11). Microarray clustering thus overall confirmed the transcriptome remodeling effect of AEDs.

dIscussIonWe have shown here that AEDs normalize long term behavioral and transcriptomic alterations induced by PTZ withdrawal in a Drosophila model. Our evidence further suggests that AEDs’ transcriptomic effect is mediated by neutralization of upregu-lated genes related to various processes including gene expres-sion and cell communication. Furthermore, our results suggest that AEDs neutralize upregulation of genes belonging to several pathways. In the cell communication category, these pathways are inositol phosphate metabolism, phosphatidylinositol signal-ing, endocytosis, and hedgehog signaling. In the gene expression

category, these pathways are spliceosome, RNA degradation, and ribosome. It is important to note here that cell communication pathways have previously been implicated in the therapeutic mechanisms of AEDs and antipsychotic drugs in diverse stud-ies. For example, biochemical and neurobiological evidence has earlier suggested that NaVP and LEV inhibit inositol metabo-lism and/or phosphatidylinositol signaling (Simister et al., 2007; Xu et al., 2007; Nagarkatti et al., 2008; Tokuoka et al., 2008; Teo et al., 2009; Yamamura et al., 2009). Also, cell biological evidence has shown NaVP induced reduction in endocytosis, a process that is linked to phosphatidylinositol signaling (Xu et al., 2007). Similarly, transcriptomic evidence showing increased expression of endocytosis related genes in phenytoin resistant kindled rats, a model of epileptogenesis, has previously been presented (Zeng et al., 2009). Further, several antipsychotic drugs have recently been found to regulate hedgehog signaling (Lauth et al., 2010), a pathway that, besides its established role in adult CNS, is also known for its growth enhancing effect in the adult brain (Tsuboi and Shults, 2002; Dellovade et al., 2006).

Regarding gene expression related pathways, though transcrip-tion factors and mRNA expression have earlier been implicated (Atmaca, 2009; Heinrich et al., 2009; Christensen et al., 2010;

Figure 7 | genes mapping to Kegg pathway for inositol phosphate metabolism. Pink boxes represent upregulated genes in AED untreated flies. These genes are IP3K2 (Inositol 1,4,5-triphosphate kinase 2), Ipk1 (CG30295 gene

product from transcript CG30295-RB), Ipp (Inositol polyphosphate 1-phosphatase), and PIP5K59B (CG3682 gene product from transcript CG3682-RA). Green boxes represent other members in the pathway database for Drosophila melanogaster.

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a subset of the total genes represented on the microarrays, due to the loss of a large number of genes during downstream processing of the data. More sensitive labeling, hybridization, and detection system, such as those provided by Affymetrix platform, could have possibly minimized this loss and resulted in the identification of additional differentially expressed genes and, in turn, enrichment of additional processes and pathways.

Acute biochemical effects of drugs used in treating neuropsy-chiatric disorders do not provide simple mechanistic interpre-tations for the observed neuroprotection (Zhou et al., 2009). Instead, genomic effects of these drugs are considered to ulti-mately translate into the overall neuroprotection (Altar et al., 2009). Given this, it is significant that transcriptomic analysis in a novel Drosophila model has provided evidence that is consist-ent with known effect of drugs reported in mammalian studies. Amenable to various experimental approaches, the Drosophila model may thus offer a unique opportunity to further under-stand the psychotropic drug mechanism in cellular and molecular

Girgenti et al., 2010), the involvement of spliceosome, RNA deg-radation, and ribosomal pathways has not been well documented in the therapeutic mechanism of AEDs. It is therefore notable that our study has identified these three pathways as additional candi-dates in the therapeutic mechanism. Altered regulation of RNA metabolism including splicing, mRNA stability, etc., are known to be associated with various neurological and psychiatric disor-ders (Licatalosi and Darnell, 2006; Anthony and Gallo, 2010). Also, recent biochemical studies have shown that phosphatidylinositol signaling is linked to mRNA processing and translation (Kiefer et al., 2009; Laserna et al., 2009; Lewis et al., 2011). The above evidence supports our results that implicate a spectrum of gene expression and cell communication pathways in the therapeutic mechanism of neuropsychiatric disorders. It is intriguing though that we did not find enrichment of processes related to synaptic function in differentially expressed genes, as would be expected for proconvulsant and anticonvulsant drugs. It may however be noted here that our transcriptomic results are based on the analysis of only

Figure 8 | genes mapping to Kegg pathway for phosphatidylinositol signaling system. Pink boxes represent upregulated genes in AED untreated flies. These genes are IP3K2 (Inositol 1,4,5-triphosphate kinase 2), Ipk1 (CG30295 gene

product from transcript CG30295-RB), Ipp (Inositol polyphosphate 1-phosphatase), and PIP5K59B (CG3682 gene product from transcript CG3682-RA). Green boxes represent other members in the pathway database for Drosophila melanogaster.

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Figure 9 | genes mapping to Kegg pathway for endocytosis. Pink boxes represent upregulated genes in AED untreated flies. These genes are Gap69C (GTPase-activating protein 69C), Hsp68 (Heat shock protein 68), PIP5K59B (CG3682 gene product from transcript CG3682-RA), TSG101 (tumor suppressor

protein 101), Vps20 (Vacuolar protein sorting 20), alpha-Adaptin (CG4260 gene product from transcript CG4260-RA), cenG1A (centaurin gamma 1A), and schizo (K12495 IQ motif and SEC7 domain-containing protein). Green boxes represent other members in the pathway database for Drosophila melanogaster.

Figure 10 | genes mapping to Kegg pathway for hedgehog signaling. Pink boxes represent upregulated genes in AED untreated flies. These genes are Rab23 (CG2108 gene product from transcript CG2108-RA), dpp (decapentaplegic), and fu (fused). Green boxes represent other members in the pathway database for Drosophila melanogaster.

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acknowledgMentsThe research was supported by NWP0034 grant of Council of Scientific and Industrial Research (CSIR), Government of India. Junior and Senior Research Fellowships to Farhan Mohammad and Priyanka Singh from CSIR is duly acknowledged.

author contrIButIonsPriyanka Singh performed the experiments. All authors analyzed

the data. Abhay Sharma wrote the manuscript.

suppleMentary MaterIalThe Supplementary Material for this article can be found online at http://frontiersin.org/neuroscience/neurogenomics/paper/10.3389/fnins.2011.00161/abstract

terms. Methods in fly genetics, for example, may be used to func-tionally validate candidate genes in the pathways. Similarly, epige-netic approaches could be applied to understand long term drug effects in brain plasticity. Besides, the fly model may directly be used for drug screening using behavioral and functional genomic readouts. It is tempting to note here that we are currently imple-menting the fly model in screening drugs approved by Food and Drug Administration (USA) toward potential repurposing. The preliminary results obtained so far are indeed encouraging. Further screening of positive compounds in a battery of rodent models available for epileptogenesis, and neuropsychiatric and neurodegenerative disorders will be crucial. In brief, the fly model promises to accelerate the pace of discovery in the area of CNS disorders and therapy.

Figure 11 | Hierarchical clustering of microarrays. Microarrays of 10th and 14th day time-points are clustered to examine separation of profiles of flies untreated with an AED and flies treated with the AEDs ETH (A), NaVP (B), LEV (C), VGB (D), and GBP (e) after PTZ withdrawal.

Note clear separation of the two groups. The AED untreated group is indicated by PTZ, and the AED treated groups by AED abbreviation. Jaccard similarity metric and average linkage methods were used for clustering.

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implicated and novel pathways in the thera-peutic mechanism in neuropsychiatric dis-orders. Front. Neurosci. 5:161. doi: 10.3389/fnins.2011.00161This article was submitted to Frontiers in Neurogenomics, a specialty of Frontiers in Neuroscience.Copyright © 2011 Singh, Mohammad and Sharma. This is an open-access article sub-ject to a non-exclusive license between the authors and Frontiers Media SA, which per-mits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.

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Conflict of Interest Statement: The authors declare that the research was con-ducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 10 November 2010; accepted: 23 February 2011; published online: 31 March 2011.Citation: Singh P, Mohammad F and Sharma A (2011) Transcriptomic analysis in a Drosophila model identifies previously

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